Kubernetes

In a previous blog we reviewed how to create and manage EKS Clusters on AWS. Apperati.io. In particular we discussed: How to use a simple tool from Weaveworks eksctl to setup and use EC2 nodes, network, security, and policies to get your cluster up. Providing access to the EKS cluster and how to use a easy but non-scalable configuration to provide access (modifying aws-auth configmap in the EKS cluster). Showcased Day 2 operations with respect to cost and utilization, security in AWS, and observability.

This post represents an interesting departure for me. Typically, I cover technical topics related to applications, Kubernetes, and portions of the “cloud native” world. In this piece, however, I’d like to address an issue much more steeped in people and process concerns; cloud operations. My colleagues and I spend quite a bit of time interacting with administrators and operators representing a wide variety of organizations. Whether these conversations begin with a particular set of technologies or projects, the discussion inevitably turns to the differences in operating across public cloud and software-as-a-service environments.

Deploying applications into the cloud is the norm. Majority of these applications are landing on AWS, GCP or Azure. In addition, more and more of these applications are also using containers and utilizing Kubernetes. Kubernetes is becoming more mainstream and the “mainstay” in many organizations. Adoption is growing, as are the number of options for Kubernetes. There are many Kubernetes choices to deploy your containerized application: Custom deployment solutions - from VMware Essential PKS, Kubespray, VMware Enterprise PKS, Stackpoint, etc.

In a previous blog we discussed the configuration and use of fluentbit with AWS elasticsearch. https://medium.com/@bahubalishetti/configuring-fluentbit-on-kubernetes-for-aws-elasticsearch-bec486bcc727 It helped provide a basic configuration of “logging” from a Kubernetes cluster. “Logging” is one aspect of “Observability” in Kubernetes. Lets review: Observability for the cluster and the application covers three areas: Monitoring metrics — Pulling metrics from the cluster, through cAdvisor, metrics server, and/or prometheus, along with application data which can be aggregated across clusters in Wavefront by VMware.

As noted in one of my earlier blogs, one of the key issues with managing Kubernetes is observability. Observability is the ability to gain insight into multiple data points/sets from the Kubernetes cluster and analyze this data in resolving issues. As review, observability for the cluster and application covers three areas: Monitoring metrics — Pulling metrics from the cluster, through cAdvisor, metrics server, and/or prometheus, along with application data which can be aggregated across clusters in Wavefront by VMware.